Plant protein-based Pickering emulsions (PPPEs) have demonstrated excellent performance in improving the quality of meat products. In-depth research on the interfacial behavior and action mechanisms of PPPEs facilitates quality improvement and green transformation in the meat product industry. Although plant proteins can stabilize Pickering emulsions, challenges remain in ensuring emulsion stability, achieving compatibility between plant proteins and the emulsion system, and enhancing the texture and flavor of final meat products. This paper provides a systematic review of the current advancements of PPPEs in the meat products industry. It covers the research progress in improving the quality of meat products by combining plant proteins with Pickering emulsions, focusing on texture modification, flavor enhancement, and nutritional fortification. Additionally, the challenges and prospects of research in the application of PPPEs are explored in meat product processing. Integrating emerging plant protein resources, green processing and preparation methods, sous-vide cooking (SV), and 3D printing with other advanced technologies could improve the quality of meat products to enable the development of new products. In the future, PPPEs are expected to play a crucial role in enhancing the nutritional value of meat products and promoting the greening and sustainable development of the meat industry.

Complex and variable food systems contain numerous interfering substances that produce intricate analytical signals within the relevant matrices. Analytical detection utilizing optical nanosensing often generates an extensive array of data points, characterized by high dimensionality or complex imaging maps. Consequently, the extraction of meaningful data from these vast datasets has emerged as a significant challenge. The integration of machine learning with optical nanomaterials has exhibited remarkable predictive capabilities and accuracy in the processing, analysis, and extraction of valuable information from large and complex food-related datasets. This review systematically elucidates the synergistic relationship between optical nanosensing and machine learning, encompassing: (1) the signal transduction mechanisms and data acquisition utilizing optical nanomaterials; (2) the design and optimization of nanomaterials driven by machine learning; (3) the applications of machine learning algorithms for food data processing and predictive modeling; and (4) the advancements in collaborative applications of machine learning and optical nanomaterials in food quality analysis. Building on this foundation, the paper addresses the challenges currently confronting research on the application of machine learning-driven optical nanomaterials in food quality analysis, constructs a research framework, and outlines potential directions for future exploration. This review offers novel insights into the integrated application of optical nanomaterials and machine learning, presents innovative solutions for the rapid and real-time detection of food quality, and significantly enhances the depth of research into food quality assessment.

Plant flavonoids have received considerable attention for their health benefits. However, structure-activity relationships between flavonoids such as orientin and vitexin with similar structures are rarely reported. In the present study, molecular docking study suggested that orientin and vitexin suppressed inflammatory responses through the modulation of the mitogen-activated protein kinase (MAPK) signaling pathway. Moreover, RAW264.7 cells with lipopolysaccharide-induced inflammation were used to evaluate the anti-inflammatory activities of orientin and vitexin, based on the inflammation-related cytokines production, quantitative real-time reverse transcription PCR, and western blotting analysis. As a result, orientin or vitexin attenuated inflammatory responses through modulation of the MAPK/NF-κB signaling pathway by suppressing NF-κB translocation.